András Rövid

3D model estimation from multiple images

3D reconstruction plays a very important role in computer vision. The determination of the 3D model from multiple images is of key importance. A 3D reconstruction algorithm is introduced, which is capable to determine the 3D model without any external intervention.

On tensor-product model based representation of neural networks

The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a novel tensor-product based approach for representation of neural networks (NNs) is proposed. The NNs in this case stand for local models based on which a more complex parameter varying model can numerically be reconstructed and reduced using the higher order singular value decomposition (HOSVD). The HOSVD as well as the tensor-product based representation of NNs will be discussed in detail.

The HOSVD Based Canonical Form of Functions and Its Applications

The paper deals with the theoretical background of the higher order singular value decomposition (HOSVD) based canonical form of functions. Furthermore in special case it describes the relation between the canonical form and the Hilbert-Schmidt type integral operators. The described techniques have a variety of applications, e.g. image processing, system identification, data compression, filtering, etc. As an example of application from the field of intelligent systems, a tensor-product based concept is introduced useful for approximating the behavior of a strongly non-linear system by locally tuned neural network models. The proposed approach may be a useful tool for solving many kind of black-box like identification problems. The weights in the corresponding layers of the input local models are jointly expressed in tensor-product form such a way ensuring the efficient approximation. Similar concept has been used by the authors for approximating the system matrix of linear parameter varying systems in state space representation. We hope that the proposed concept could be an efficient compromised modeling view using both the analytical and heuristic approaches.

Method for merging multiple exposure color image data

In most of nowadays intelligent systems (robots, vehicles, etc.) the sensing based on image processing represents an essential part. In order to be able to use these systems effectively even if the lighting conditions are not ideal, e.g. dark objects, very bright areas in the scene, etc., multiple exposure based image reproduction algorithms are highly welcome. The main aim of the paper is to introduce a novel method enabling to merge multiple exposure images and obtain a corresponding High Dynamic Range Image.

Multi-core processor needs from scheduling point of view

According to the current processor trend, the cores of the processors are increasing, which ensures much higher computational capacity in multithreaded environments. Schedulers are prepared for multi processor environment, but scheduling algorithms are still based on the single processor scheduling approach. In this paper the reasons of changing the current scheduling approaches and them explanations will be discussed. The main aim of this analysis is to give support by the development of new qualitative scheduling approaches.

Numerical Reconstruction and Compression of Thermal Image Sequences

Data compression and enhancement represent an important consideration in many application areas. The transmission bandwidth and storage capacity are often crucial factors. The efficiency of the processing of data in numerous cases strongly depends on the form of data representation. In the present paper a method, based on the so called higher order singular value decomposition and tensor-product transformation, is introduced for multidimensional scaling and compression of thermal images sequences. The proposed approach operates with smooth functions forming an orthonormal basis. Because of the smoothness property of these orthonormal components, the method can advantageously be utilized for the efficient compression of thermal image sequences.

Control model for loading systems using higher order singular value decomposition

In this paper we present a method for constructing a control model of loading systems based on higher order singular decomposition (HOSVD) of a tensor. Modern logistics need control systems that are able to develop and improve the material and information flow by automating the control processes. We propose the application of linear parameter varying (LPV) structure by which non-linear systems can be controlled on the basis of linear control theories. Describing the system, modeling logistic processes require many uncertain input parameters. Using the proposed method the complexity of the model can be kept on a lower level.

Controlling-Observing Interpretation of the Fine Structure Constant

The paper discusses formulas and interpretations of the fine structure constant (FSC) and the Number Archetype 137 taking into account of our earlier results and the concept of the “undetached observer” of Pauli. A new “controlling-.observing equation” is proposed for the reinterpretation of FSC and other Number Archetypes on the basis of Hermeneutics found in W. Pauli and C. G. Jung “Correspondence”.

Detect Scenic Leaves and Blossoms Viewing Places from Flickr Based on Social and Image Features

Photo sharing websites have become extremely popular with GPS-enabled digital cameras and camera phones. Seeing autumn leaves, cherry blossoms, etc. is a traditional seasonal activity. Tremendous photos about leaves and blossoms have been uploaded. The goal of our paper is to detect and rank the most scenic leaves and blossoms viewing places based on social and image features. Methods for social interestingness, color percentage, edge rate, sharpness, ranking score have been provided. The proposed methods have been validated and verified by experimental results for maple leaves in Kyoto.

Thermal Image Processing Approaches for Security Monitoring Applications

A time series collected by thermal cameras yields many application possibilities for security monitoring applications. The character of features involved in thermal images is different compared to the images acquired by conventional capturing devices; many methods applicable for conventional image processing cannot directly be applied for thermal images. New methods are introduced in HOSVD based representation of thermal images, and in application of resolution enhancement and data compression. The retrieval process of thermal image databases and useful feature descriptors were analyzed, as well as the special semi-automatic fusion techniques of thermal image sequences.